Practical overview
When it fits
Use Markus when a goal benefits from decomposition, parallel execution, persistent context, and formal review rather than a single one-off assistant response.
Who it is for
- Teams building multi-agent project execution workflows
- Developers creating AI software teams with specialized implementation and review roles
- Organizations exploring self-hosted agent orchestration
- Builders of AI-driven content or operations workflows
Documented uses
- Turn a plain-language goal into assigned and decomposed tasks
- Run work across specialist agents in parallel
- Coordinate agent-to-agent activity and isolated workspaces
- Review and gate agent deliveries
- Maintain context and memory across sessions
Requirements & limits
The project is described as open source under the Apache-2.0 license. Documented access paths include a local CLI installation, a bundled web interface, and desktop releases for macOS, Windows, and Linux.
- Whether the workflow needs multiple specialist agents instead of one assistant
- Whether persistent procedural, semantic, and episodic memory is useful
- Whether parallel execution with isolated per-session workspaces is required
- Whether submit–review–merge governance and audit trails matter
- Whether self-hosting, SQLite or PostgreSQL storage, and bring-your-own model credentials fit the deployment
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Start Markus locally with the bundled web interface
Install Markus and open its local onboarding interface.
Before you start
- Node.js 22+ for the documented npm installation path.
- A local Markus deployment using the repository's command-line package.
Steps
Install the global CLI with the command below.
npm install -g @markus-global/cliRun
markus start.Open
http://localhost:8056and use the onboarding wizard to create the account.Submit a plain-language goal to the Secretary so Markus can assemble agents, split tasks, and begin parallel execution.
Expected outcome
A local Markus interface is available for submitting a goal to an AI agent team.
Guide limitations
- The documented setup path does not establish that a particular model provider or external tool is configured.
- The guide describes starting the interface and submitting a goal, not the outcome of a specific project run.
Compare options for Agent orchestration
These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.
| Project | Documented purpose | Platform | Access and requirements |
|---|---|---|---|
| Markus | Turn a plain-language goal into assigned and decomposed tasks | Cloud, Self-hosted, Server | The project is described as open source under the Apache-2.0 license. Documented access paths include a local CLI installation, a bundled web interface, and desktop releases for macOS, Windows, and Linux. |
| Massgen | Run multiple AI agents against the same user task. | CLI | The repository documents installation through pip or uv and requires Python 3.11 or later. Setup can configure API keys for OpenAI, Anthropic, Google and xAI; credentials are needed for the selected model backends. |
| Langchain Multi Agents Boilerplate | Route incoming football questions to model and agent workflows according to task complexity. | Web | The repository is public and specifies an MIT license. Running the application requires OpenAI and Groq API keys and a Redis server. |
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 30 September 2026
- Original website title
- markus-global/markus · Official homepage title · retained in its source language